Promise Constraint Satisfaction and Width

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Delves into the theory of computation, covering decidability, complexity, P vs. NP, and reductions.

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Covers classical and quantum computational complexity concepts and implications.

Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.

Explores algorithmic complexity, comparing growth rates using Theta notation and characterizing different complexity classes.

Covers algorithmic complexity and travel time analysis, focusing on measuring the time taken by algorithms and evaluating their performance.

Covers the basics of algorithms, problem-solving, and efficient resolution methods.

Explores critical phenomena in complex systems, including stochastic objects, percolation, and combinatorial optimization.

Examines NP problems, graph coloring, path optimization, and computational complexity distinctions in P and NP classes.

Introduces complexity classes, including P and NP, and explores examples of easy and hard problems.